In this paper, we propose a general Substructural Domain Adaptation (SSDA) method.
SSDA is a general framework consistent with the substructure and  REF  illustrates the main process of SSDA which mainly contains three steps. Firstly, SSDA clusters the data to obtain the substructures of activities. As a general framework, we can choose a suitable clustering algorithm for customization. Then, it gives weights to the source substructures according to priors or adaptive methods. Weight represents the importance of substructures and different substructures often play different roles. For example, some substructures far away from most data should play small roles with small weights. For simplicity, we usually give uniform weights to all structures without priors. Finally, mapping is performed on the substructures of different domains.
We can extend some traditional method, such as CORrelation alignment (CORAL) [1]}, to perform substructure-level matching, which is really commendable.
